ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2024
Predicting HIV Diagnosis Among Emerging Adults Using Electronic Health Records and Health Survey Data in All of Us Research Program.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.medRxiv : the preprint server for health sciences · 2026Article
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The global decline in HIV incidence has not been mirrored in the United States, where young adults (ages 18-29) continue to account for a significant portion of new infections. In this study, we leverage the All of Us (AoU) Research Program's extensive electronic health records (EHRs) and health survey data to develop machine learning models capable of predicting HIV diagnoses at least three months before clinical identification. Among various models tested, the Support Vector Machine (SVM) model demonstrated a balanced performance, integrating clinically relevant features with robust predictive accuracy (AUC = 0.91). Risky drinking behaviors emerged as consistent top predictors across models, highlighting the importance of targeted interventions in this age group. Our findings underscore the potential of predictive analytics in enhancing HIV prevention strategies and informing public health efforts aimed at reducing HIV transmission among emerging adults.
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Registered trials
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